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Detection, Estimation modulation theory part 1 - Vantress H.

Vantress H. Detection, Estimation modulation theory part 1 - Wiley & sons , 2001. - 710 p.
ISBN 0-471-09517-6
Download (direct link): sonsdetectionestimati2001.pdf
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Colored noise problem
Possible approaches: Karhunen-Lo6ve expansion, prewhitening filter, generation of sufficient statistic, 289
Reversibility proof (this idea is used several times in the text), 289-290 Whitening derivation, 290-297
Define whitening filter, hw(t, u), 291 Define inverse kernel Qn(t, u) and function for correlator g(t), 292
Derive integral equations for above functions, 293297
Draw the three realizations for optimum receiver (Fig. 4.38), 293 Construction of Qn(t, u)
Interpretation as impulse minus optimum linear filter, 294-295 Series solutions, 296-297 Performance, 301-307
Expression for d2 as quadratic form and in terms of eigenvalues, 302
Optimum signal design, 302-303 Singularity, 303-305
Importance of white noise assumption and effect of removing it
Duality with known channel problem, 331-333
Assign as reading,
1. Estimation (4.3.5)
2. Solution to integral equations (4.3.6)
3. Sensitivity (4.3.7)
4. Known linear channels (4.3.8)
Problem Set 7 (continued)
4. 4.3.4 7. 4.3.12
5. 4.3.7 8. 4.3.21
6. 4.3.8
652 Appendix: A Typical Course Outline
4.4.1
4.4.2
45-4.7
Lecture 16 pp. 333-377
Signals with unwanted parameters, 333-366
Example of random phase problem to motivate the model, 336
Construction of LRT by integrating out unwanted parameters, 334
Models for unwanted parameters, 334
Random phase, 335-348
Formulate bandpass model, 335-336 Go to A(r(t)) by inspection, define quadrature sufficient statistics Lc and Ls, 337 Introduce phase density (364), motivate by brief phaselock loop discussion, 337 Obtain LRT, discuss properties of In I0(x), point out it can be eliminated here but will be needed in diversity problems, 338-341 Compute ROC for uniform phase case, 344 Introduce Marcumís Q function Discuss extension to binary and M-ary case, signal selection in partly coherent channels
Random amplitude and phase, 349-366
Motivate Rayleigh channel model, piecewise constant approximation, possibility of continuous measurement, 349-352
Formulate in terms of quadrature components, 352 Solve general Gaussian problem, 352-353 Interpret as filter-squarer receiver and estimator-correlator receiver, 354 Apply Gaussian result to Rayleigh channel, 355 Point out that performance was already computed in Chapter 2
Discuss Rician channel, modifications necessary to obtain receiver, relation to partially-coherent channel in Section 4.4.1, 360-364
Assign Section 4.6 to be read before next lecture; Sections 4.5 and 4.7 may be read later, 366-377
Lecture 16 653
Lecture 16 (continued)
Problem Assignment 8
1. 4.4.3 5. 4.4.29
2. 4.4.5 6. 4.4.42
3. 4.4.13 7. 4.6.6
4. 4.4.27
654 Appendix: A Typical Course Outline
Lecture 17 pp. 370-460
4.6 Multiple-parameter estimation, 370-374
Set up a model and derive MAP equations for the
colored noise case, 374
The examples can be left as a reading assignment but
the MAP equations are needed for the next topic
Chapter 5 Continuous waveform estimation, 423-460
5.1, 5.2 Model of problem, typical continuous systems such
as AM, PM, and FM; other problems such as
channel estimation; linear and nonlinear modula
tion, 423-426
Restriction to no-memory modulation, 427
Definition of am&v(r{t)) in terms of an orthogonal
expansion, complete equivalence to multiple para
meter problem, 429-430
Derivation of MAP equations (31-33), 427-431
Block diagram interpretation, 432-433
Conditions for an efficient estimate to exist, 439
5.3-5.6 Assign remainder of Chapter 5 for reading, 433-460
Lecture 18
Lecture 18 655
pp. 467-481
Chapter 6 6.1
Linear modulation
Model for linear problem, equations for MAP interval estimation, 467-468
Property 1: MAP estimate can be obtained by
using linear processor; derive integral equation, 468 Property 2: MAP and MMSE estimates coincide
for linear modulation because efficient estimate exists, 470 Formulation of linear point estimation problem, 470 Gaussian assumption, 471 Structured approach, linear processors
Property 3: Derivation of optimum linear pro-
cessor, 472
Property 4: Derivation of error expression [em-
phasize (27)], 473 Property 5: Summary of information needed, 474 Property 6: Optimum error and received wave-
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